Impact analysis

ABSTRACT

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for impact analysis. One of the methods includes receiving information about at least two logical datasets, the information identifying, for each logical dataset, a field in that logical dataset and format information about that field. The method includes receiving information about a transformation identifying a first logical dataset from which the transformation is to receive data and a second logical dataset to which the transformed data is provided. The method includes receiving one or more proposed changes to at least one of the fields. The method includes analyzing the proposed changes based on information about the transformation and information about the first logical dataset and the second logical dataset. The method includes calculating metrics of the proposed change based on the analysis. The method also includes storing information about the metrics.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application Ser.No. 62/075,558, filed on Nov. 5, 2014, entitled “IMPACT ANALYSIS,” theentire contents of which are hereby incorporated by reference.

BACKGROUND

This description relates to system analysis.

Computers are used to process large amounts of data. In general, thedata is processed using computer programs what are written, at least inpart, by computer programmers. These data processing systems can becomplex.

Business and technical requirements can require that programs change.Implementing a change requires the allocation of personnel to make thechange.

SUMMARY

In general, according to aspect 1, a method includes the actions ofreceiving information about at least two logical datasets, the logicaldataset information identifying, for each logical dataset, an identifierfor at least one field in that logical dataset, and format informationabout that field. The method includes the actions of receivinginformation about a transformation, the information identifying a firstlogical dataset describing characteristics of a first physical datasetfrom which the transformation is to receive data and a second logicaldata describing characteristics of a second physical dataset to whichthe transformed data is to be provided. The method includes the actionsof receiving one or more proposed changes to at least one of the fieldsof a logical dataset. The method includes the actions of analyzing theone or more proposed changes based on information about thetransformation and information about the first logical dataset and thesecond logical dataset. The method includes the actions of calculatingone or more metrics of the proposed change based on the analysis. Themethod includes the actions of storing information about the one or moremetrics.

Other embodiments of this aspect include corresponding computer systems,apparatus, and computer programs recorded on one or more computerstorage devices, each configured to perform the action of the methods. Asystem of one or more computers can be configured to perform particularactions by virtue of having software, firmware, hardware, or acombination of them installed on the system that in operation causes thesystem to perform the actions. One or more computer programs can beconfigured to perform particular actions by virtue of includinginstructions that, when executed by data processing apparatus, cause theapparatus to perform the actions.

The methods include an aspect 2 according to aspect 1 wherein thecalculated metric provides a measure of direct impact. The methodsinclude an aspect 3 according to aspects 1 or 2 wherein the calculatedmetric provides a measure an indirect impact. The methods include anaspect 4 according to aspects 1, 2, or 3 wherein the proposed change isone of the group consisting of a change in format of a field in adataset or a change in a transformation. The methods include an aspect 5according to aspects 1, 2, 3, or 4 wherein the transformation includesone or more rules to be applied to data from the first logical dataset,and wherein analyzing the one or more proposed changes is further basedon the one or more rules. The methods include an aspect 6 according toaspects 1, 2, 3, 4, or 5, wherein the method further includes theactions of Other embodiments of this aspect include associating a costto the proposed change based on the metric.

Aspects can include one or more of the following advantages. The scopeand cost of making a change can be estimated. Locations where a changewill affect a program can be identified. Resources can be appropriatelyallocated.

Other features and advantages of the invention will become apparent fromthe following description, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 illustrates an example of a data lineage for multiple executableprograms.

FIG. 2 illustrate an example rule set and the inputs into the rules set.

FIG. 3 illustrates an example of a human readable rule set.

FIG. 4 illustrates a process by which a rule can be transformed from thehuman readable form into a transformation containing machine readablecode.

FIG. 5 illustrates a report generator generating a report based onannotated code.

FIG. 6 shows an example of a data processing system in which the impactanalysis techniques can be used.

FIG. 7 is a flowchart of an exemplary impact analysis procedure.

DESCRIPTION

Generally, data processing systems can read data from a source, performoperations on the data to produce new data, and store the new data in adata store. Data processing systems range in complexity from trivial toextremely complex. In more complex systems, changes made to a dataprocessing system can have wide ranging effects that can be difficult todetermine. In order to determine the impact of making a change to asystem, it is helpful to determine the parts in the system that aredirectly affected by the change, and the parts in the system that areindirectly affected by the change. In general, parts in the system thatare directly affected by the change may require an individual tomanually adjust the system. For example, a programmer may be required tochange the contents and behavior of an application. In general, parts ofthe system that are indirectly affected by the change may require thatthose parts be tested to ensure that the changes made by the programmerhave not adversely affected the behavior of the application.

In a complex system, it can be difficult to determine the scope of aproposed change. Some changes may be cost prohibitive, while otherchanges may be relatively inexpensive. In a complex system, it isfrequently difficult to tell the difference between a cost prohibitivechange and a relatively inexpensive one. Some changes may be necessaryto increase the quality of the process; however, it is still necessaryto be able to estimate the scope of the work that must be undertaken toimplement the change. Further, it's important to provide consistent,reproducible, and predictable quotes. Further quotes provided by thesystem can be compared to human estimates and actual results.

In order to determine, the scope of a change, a system is analyzed. Theanalysis identifies how data flows through the system. The system isalso analyzed to identify how a change to either the data used by thesystem or the processing of that data could affect other parts of thesystem.

FIG. 1 illustrates an example of a data lineage 100 for multiplecomponents. Components can include logical datasets and transforms. Atransform can be, for example, dataflow graphs, java programs, compiledexecutable programs, or any combination thereof. In general, a transformcan accept input data and can generate output data. For example, graph 1104 accepts input data from logical dataset 1 102 and logical dataset 2103 and generates output data that is provided to logical dataset 2 106.

In general, a physical dataset refers to application data stored on acomputer-readable medium, including but not limited to magnetic disks,flash memory, random access memory, and read only memory. The physicaldataset may include unique data that can vary from day to day. Ingeneral, logical datasets is a data structure that acts as anabstraction of one or more physical datasets that store data. In someimplementations, a physical datasets with different data may be storedin separate files. For example, a physical dataset of foreign exchangerate data for November 8 may be stored in a file, “ExchangeRate_11_08.”The physical dataset of foreign exchange rate data for November 9 may bestored in a file, “ExchangeRate_11_09.” Both the physical dataset forexchange rate data for November 8 and the physical dataset for exchangerate data for November 9 have common elements. For example, bothphysical datasets share a common data format. This common data formatmay be reflected in a logical dataset that is associated with both the“ExchangeRate_11_08” file and the “ExchangeRate_11_09” file.

In general, logical datasets include information about the attributes ofthe dataset that do not vary between physical dataset. For example,information about logical datasets may include field names, data types,record formats, constraints, and other features. Logical datasets can becategorized as data sources and data sinks A single logical dataset canbe a data sink for one transform and a data source for anothertransform. The corresponding physical datasets of a logical dataset canbe, for example, a table in a relational database or a file on a filesystem (among other places). The data source can read the data recordsstored in the logical dataset and can create in-memory data records.Components accept the in-memory data records created by the data sourceand modify or transform the data. Data values can be changed ortransformed. New data records can be created. A data sink can provide anexit point from the dataflow graph and can store output records. Likethe data source, a data sink can be, for example, a relational databasetable or a file stored on a file system. The components can be executedon a computer or other type of computer device. In otherimplementations, the execution of the dataflow graph can be distributedamong multiple computing devices.

In some implementations, components can accept input data, for exampleon input ports, and produce output data, for example on output ports.Links connect an output port of a first component to an input port of asecond component. Some components can have multiple input and outputports. The sequence of components and links by which a data record cannavigate from an entry point to an exit point is referred to as a path.Data lineage can be used to identify the different paths and trace theflow of data through one or more components.

In this example the data element ‘x’ 102 is a member of logical dataset1. The data element ‘y’ 103 is a member of logical dataset 2. Ingeneral, a data element refers to individual records stored within alogical dataset. For example, a logical dataset can be a table in arelational database and a data element can be a row from that table.Data element ‘x’ and data element ‘y’ are input into graph 1 104. Graph1 generate logical dataset 2 106. Logical dataset 2 contains dataelements ‘A’ 108 and data element ‘B’ 110. These data elements areinputs into graph 2 112. Data element ‘A’ is used to generate dataelement ‘C’ 114. Data element ‘C’ is provided as an input into rule set1 116. In general, a rule set is a collection of rules that are appliedto data to generate an output. A rule set can be, for example, a seriesof tests and results that are applied to a value in a data element. Arule set can accept one or more inputs and based on the values of thoseinputs produce one or more outputs. In general, a rule set can becompiled or made into a computer executable transformation. The datalineage graph 100 shown in FIG. 1 has been simplified for explanatorypurposes and space considerations. In general, the presence of anellipsis along a line indicates that one or more components and datasources have been omitted. Data transformations may occur that are notshown. For example, data element ‘A’ 108 may be transformed to generatedata element ‘C’ 114. Data element ‘E’ 118 may be transformed togenerate data element ‘G’ 122, etc.

Rule set 1 generates two outputs data element ‘E’ 118 and data element‘F’ 120. Data element ‘E’ 118 is used to generate data element ‘G’ 122.Data element ‘G’ is provided as input to rule set 2 130. Rule set 2produces an output of data element ‘I’ 132. Data element ‘I’ is used togenerate data element ‘J’ 140 of logical dataset 3 138. Data element ‘F’120 is used to generate data element ‘H’ 124 and data element ‘D’ 126.Data element ‘B’ 110 is used to generate data element ‘M’ 128. Dataelement ‘M’ 128 and data element ‘D’ 126 are provided as input to ruleset 3 314. Rule set 3 generates data element ‘K’ 136. Data element ‘K’is used to generate data element ‘L’ 142 of logical dataset 3 138. Dataelement ‘Y’ is provided as an input to rule set 4 144. Rule set 4 144generates data element ‘N’ 146 of logical dataset 3 138.

A change made to a logical dataset or data element can affect manydifferent rule sets and data elements. These changes can include, amongother changes, a change to a schema or record format and changes tovalid values for different data elements. For example, if a recordschema of a data element is changed (for example, the record schema canchange from a number to a string field) the change can affect each ruleset that utilizes that data element and each rule set that utilizes adata element that depends on the changed data element. For example, achange made to the record format of data element C 114 could affect ruleset 1 116, data element E 118, data element F 120, data element G 122,data element H 124, data element D 126, rule set 2 130, data element I132, data element J 140, rule set 3 134, data element K 136, and dataelement L 142. A change made to the record format of data element X 102could affect every other element in the data lineage (with the exceptionof data element Y 103, ruleset 4 144, or data element N 146).

A system can generate a report that provides information on the impactof a change to a data element or rule set. For example, the report 150provides information on the impact of a change to data element A ongraph 2.

The report 150 includes a direction column 152. Direction column 152indicates a direction in the data lineage for which the report 150 wasgenerated. The direction can be either upstream (referring to rule sets,logical datasets, and data elements that precede the data element in thedata lineage) or downstream (referring to rule sets, logical datasets,and data elements that follow the data element in the data lineage). Forexample, data element C is upstream of rule set 1 and downstream of dataelement A.

The report 150 also includes a graph column 154. The graph column 154identifies the graph that is the subject of a rows in the report 150. Inthis example, graph 2 112 is the subject of the report 150. The report150 also includes a graph field column 156. The graph field column 156identifies the field that is the subject of the report 150. In general,the field will be an input to the graph if the direction is downstreamand an output of the graph if the direction is upstream. In thisexample, the data element A 108 and B 110 is the subjects of the report150.

The report 150 also includes a rule set field column 158. The rule setfield column 158 identifies data elements that are inputs (in the caseof a downstream report) or outputs (in the case of an upstream report).The rule set column 160 identifies the rule set that is the subject ofthe row of the report 150. In this example, the report 150 providesinformation about data element C as an input into rule set 1 (in thefirst tow 166), data element G as an input into rule set 2 (in thesecond row 168), data element H as an input into rule set 2 (in thethird row 170), data element D as an input into rule set 3 (in thefourth row 172), and data element M as an input into rule set 4 (in thefifth row 174).

The report 150 also includes a direct column 162 and an indirect column164. The direct 162 and indirect 164 columns are determined as describedfurther below. The direct column reports the number of times that thedata element identified by the rule set field is directly referencedwithin the rule set. For example, the direct column 162 can include acount of the expressions that directly assign a value to the output. Theindirect column 164 identifies the number of times that the data elementidentified by the rule set field affects the value of one or more otherdata elements within the rule set identified by the rule set field. Forexample, the indirect column 164 can display a count of the total numberof rule cases or other expressions that contribute to the output valueof the data element. For an output that is computed by a business rule,the expression count is the number of rule cases, including a defaultvalue if there is one. In this example, data element ‘C’ (row 166) isdirectly references 13 times in rule set 1 and affects the value of oneor more other data elements 70 times.

In order to generate the report 150, a system processes a rule set todetermine which data elements are relevant to the rule set. The rule setcan be expressed as a set of criteria that can be used, for example, forconverting data from one format to another, making determinations aboutdata, or generating new data based on a set of input data.

FIG. 2 illustrates an example rule set and the inputs into the rule set.As described above, rule set 3 134 has two inputs, data element ‘D’ 126and data element ‘M’ 128. Rule set 3 134 may reference the inputs as a“Years” parameter 202 and an “Income” parameter 204. In this example,the “Years” parameter 202 and the “Income” parameter 204 are processedby a transform 206, described in more detail below. The Transform 206produces a “Risk” output 208 which is provided as data element ‘D’ 136.

FIG. 3 illustrates an example of a human readable rule set 300. Thehuman-readable rule set 300 can be defined at run time using a graphicaluser interface (GUI) or can be pre-defined in a flat file or otherstructure. The human-readable rule set 300 can be later compiled into atransform, for example, the transform 206 of FIG. 2, as described below.For example, the human-readable rule set 300 can be compiled into therule set 3 134 of FIG. 1. For example, if the data element M 128represented income and the data element D 126 represented years as acustomer. Referring again to FIG. 3, the human-readable rule set 300 isillustrated in a tabular form. The human-readable rule set 300 presentedin FIG. 3, can be used to determine a risk category 310 as an output 304based on two inputs 302, namely, years 306 and income 308. In thisexample, there are seven potential conditions. A first rule 312 statesif a person has been a customer for more than 15 years, the risk is low,regardless of income. A second rule 314 states that if the customer hasan income in excess of $150,000 then the first is low, regardless of thenumber of years the person has been a customer. A third rule 316 statesthat if the person has been a customer for more than ten years (but lessthan 15) and has income of more than $60,000 then the risk is low. Afourth rule 318 states that if the person has been a customer for morethan 5 years then the risk is medium regardless of income. A fifth rule320 states that if the person has an income of greater than $50,000 thenthe risk is medium regardless of the amount of time the person has beena customer. A sixth rule 322 states that if the person has been acustomer for more than three years and has an income of more than$40,000 then the risk is low. A seventh rule 324 is a catch all rulethat states that otherwise the risk is high.

It is noted that, in this example, the rules are evaluated sequentially.Once a person qualifies for a risk category then rule processing iscomplete. For example, if a person has been a customer for more than 15years, and is assigned a risk of “low” (from row 312) then the remainingrows will never execute.

In order to determine the impact of a change to one of the input oroutput fields, a system can perform an analysis of the rule set asdescribed below.

To implement rule set in a graph-based computation environment, atransform is generated which receives input records from one or moredata sources, e.g., data element ‘C’ 106, and inserts a data elementinto an output logical dataset, e.g. data element ‘E’ 118 and dataelement ‘F’ 120. Input and output logical datasets can also be referredto as data streams. As shown in FIG. 1, the transforms can then beimplemented in graph-based computations having data processingcomponents connected by linking elements representing data flows.

FIG. 4 illustrates a process by which a human readable rule set can betransformed into a transform containing machine readable code by a dataprocessing device. A rule set 402, for example the human-readable ruleset 300 of FIG. 3, is provided to a rule generator 408. The rulegenerator generators the rule set 402 into an intermediate form. Forexample, the rule generator 406 can generate annotated code 408. Theannotated code 408 can include reported metrics 410 which defines thedirect and indirect impact of a change to a rule. For example, thehuman-readable rule set 300 can result in the generation of annotatedcode, such as

/* Default Risk Rules */ /*@ Reported Metrics: [ Output references:[default risk, 7, 0] Input references: [income, 1, 6] [years, 1, 6] ]@*/ if (years > 15) {    default_risk = “Low” } else if (income >150000) {    default_risk = “Low” } else if (years > 10 && income >60000) {    default_risk = “Low” } else if (years > 5) {    default_risk= “Medium” } else if (income > 50000) {    default_risk = “Medium” }else if (years > 3 && income > 40000) {    default_risk = “Medium” }else {    default_risk = “High” }

As discussed above, a direct impact describes the number of times thatthe data element identified by the rule set field is directly referencedor set within the rule set. Examples of measures of direct impactinclude, but are not limited to, locations in a rule set where a changedinput parameter is accessed. Examples of measure of indirect impactinclude, but are not limited to, locations in a rule set where valuesare set based on a value in a changed input parameter. The indirectimpact identifies the number of times that the data element identifiedby the rule set field affects the value of one or more other dataelements within the rule set.

A rule generator 406 can generate the direct and indirect metrics in avariety of ways. For example, in some implementations, the rulegenerator 406 can analyze the rule set to identify each time a dataelement is accessed and each time another value depends on the dataelement. More complex scenarios can also be tracked. The rule generator406 can track every variable that depends on the value of the input oroutput value regardless of how indirect. For example, if a variableaffects an intermediate value and the intermediate value affects a finalvalue, the system can record both the intermediate value and the finalvalue as indirect effects. For example, the human readable rule set 300has four rules that access the value of the years 306 input and fourrules that access the value of the income 308 input, and seven rulesthat set the value of the risk 310 output. In some implementations, arule set may be presumed to set the value for each parameter at leastonce. For example, the years input is set when the input value isprovided to the rule set.

In some implementations, the rule generator 406 can count the number ofrules in the rule set that depend, at least in part, on a parameter. Forexample, the human readable rule set 300 includes seven rules thatdepend on the years 306 input, seven rules that depend on the income 308input, and seven rules that set the risk 310 output. As discussed above,rule 324 it a catch all rule. In some implementations, catch all rulesmay be ignored by the rule generator 406.

The annotated code 408 can be provided to a rule compiler 412. The rulecompiler 412 can compile the annotated code 408 into the transform 206.In general, a transform is a machine (or virtual machine) executableprogram, for example, the executable program 416.

Referring to FIG. 4, a report generator can generate a report 408 basedon annotated code 402 for a data lineage 4. For example, referring backto FIG. 1, the system can use the data lineage to determine that, forexample, data element X affects the value of data elements A, B, C, D,E, F, G, H, I, J, K, L, and M. Therefore when processing graph 2, thesystem determines that a change to data element X implicates rule set 1,rule set 2, and rule set 3. However, data element X does not implicaterule set 4. Therefore, the report generator can determine that rule set4 does not need to be analyzed as part of the impact analysis of achange to data element X.

FIG. 5 illustrates a report generator generates a report based onannotated code. The report generator 506 identifies which inputs wouldbe affected by a change and records the results from the computedmetrics of impact. For example, data element ‘X’ affects both inputs torule set 3. Therefore, the report generator records the metrics (410 ofFIG. 4) in the report (such as rows 172, 174 of FIG. 1).

In some implementation, estimated costs can be associated with each ofthe direct and indirect counts may be determined by a data processingsystem. For example, it can be estimated that a direct effect wouldrequire a predetermined amount of a programmer's time and apredetermined amount of a quality assurance person's time. Similarly, itcan be estimated that an indirect effect would require a predeterminedamount of a quality assurance person's time. Based on the estimatedtimes, a tally of the direct and indirect effect, and a cost associatedwith a computer programmers time and a quality assurance persons time,the system can provide an estimate of the cost to make the change to theanalyzed system.

In some implementations, the rule generator can be used to assist adeveloper in identifying different portions of a system, for example, asystem represented by the data lineage 100 of FIG. 1.

FIG. 6 shows an example of a data processing system 600 in which theimpact analysis techniques can be used. The system 600 includes a datasource 602 that can include one or more sources of data such as storagedevices or connections to online data streams, each of which can storeor provide data in any of a variety of formats (e.g., database tables,spreadsheet files, flat text files, or a native format used by amainframe). An execution environment 604 includes a rule generator 606and a report generator 612. The execution environment 604 can be hosted,for example, on one or more general-purpose computers under the controlof a suitable operating system, such as a version of the UNIX operatingsystem. For example, the execution environment 604 can include amultiple-node parallel computing environment including a configurationof computer systems using multiple central processing units (CPUs) orprocessor cores, either local (e.g., multiprocessor systems such assymmetric multi-processing (SMP) computers), or locally distributed(e.g., multiple processors coupled as clusters or massively parallelprocessing (MPP) systems, or remote, or remotely distributed (e.g.,multiple processors coupled via a local area network (LAN) and/orwide-area network (WAN)), or any combination thereof.

The rule generator module 606 reads rule set from the data source 602and stores annotated code for the rules. Storage devices providing thedata source 602 can be local to the execution environment 604, forexample, being stored on a storage medium (e.g., hard drive 608)connected to a computer hosting the execution environment 604, or can beremote to the execution environment 604, for example, being hosted on aremote system (e.g., mainframe 610) in communication with a computerhosting the execution environment 604, over a remote connection (e.g.,provided by a cloud computing infrastructure).

The report generator 612 uses the annotated code generated by the rulegenerator 606 and data lineage, which can be stored in the data source602, to generate a report of the impact of making a change. The outputdata can be 614 stored back in the data source 602 or in a data storagesystem 616 accessible to the execution environment 604, or otherwiseused. The data storage system 616 is also accessible to a developmentenvironment 618 in which a developer 620 is able to determine the effectof making a change to a data element, rule, of other programmingconstruct. The development environment 618 is, in some implementations,a system for developing applications as dataflow graphs that includevertices (representing data processing components or logical datasets)connected by directed links (representing flows of work elements, i.e.,data) between the vertices. For example, such an environment isdescribed in more detail in U.S. Publication No. 2007/0051668, titled“Managing Parameters for Graph-Based Applications,” incorporated hereinby reference. A system for executing such graph-based computations isdescribed in U.S. Pat. No. 5,966,072, titled “EXECUTING COMPUTATIONSEXPRESSED AS GRAPHS,” incorporated herein by reference. Dataflow graphsmade in accordance with this system provide methods for gettinginformation into and out of individual processes represented by graphcomponents, for moving information between the processes, and fordefining a running order for the processes. This system includesalgorithms that choose interprocess communication methods from anyavailable methods (for example, communication paths according to thelinks of the graph can use TCP/IP or UNIX domain sockets, or use sharedmemory to pass data between the processes).

FIG. 7 is a flowchart of an exemplary impact analysis procedure 700. Theprocess can be performed by a data processing system, such as the dataprocessing system 600 of FIG. 6.

Information about two logical datasets is received (702). The logicaldataset information can identify, for each logical dataset, anidentifier for at least one field in that logical dataset, and formatinformation about that field.

Information about a transformation is received (704). The informationcan identify, from the two logical datasets, a first logical datasetfrom which the transformation is to receive data and a second logicaldataset to which the transformed data is to be provided. Thetransformation may include information about the rules to be applied todata from the first logical dataset. Analyzing the potential impact ofthe one or more proposed changes c further based on the one or morerules.

One or more proposed changes is received (706). A proposed change can bea change to a format of a field in a logical dataset, a change to atransformation, or a change to a rule set. In some implementations, theproposed change identifies the field in a logical dataset or thetransformation that is to be altered without specifying the nature ofthe change. For example, the proposed change can specify that field ‘X’is to be changed without indicating that the change is from a decimalrecord format to a string record format.

The proposed change is analyzed (708).

A metric of proposed change is calculated (710). The metric can measurethe impact of the change. The metric can include a measure of directimpact and/or a measure of indirect impact. Examples of measures ofdirect impact include, but are not limited to, locations in a rule setwhere a changed input parameter is accessed. Examples of measure ofindirect impact include, but are not limited to, locations in a rule setwhere values are set based on a value in a changed input parameter.

The metrics are stored (712). The metrics can be stored in a flat file,a relational database, or in any other persistent data store. Themetrics may be stored in the form of a report. The report can begenerated identifying the metric of impact. The report can associate themeasures of direct impact and the measures of indirect impact withparticular portions of the data lineage. For example, the report canindicate that a particular data flow graph, data flow graph field, ruleset field, or rule set is associated with a measure of direct impact anda measure of indirect impact.

In some implementations, the report can be tied into the data lineage,for example, through Hyper-Text Transport Protocol (HTTP) linksSelecting or clicking on the link can navigate a browser on a clientdevice to an application or website that allows the user to view aparticular portion of the data lineage. For example, referring to FIG.1, selecting or clicking on the third row 170 can cause a browser orother application on the client device to display the dataflow graph‘Graph 2 ’ 112. In some implementations, that particular graph, graphfield, rule set field, and rule set can be visually distinguished, forexample, by highlighting.

In some implementations, the report can include an average developmentand testing cost that can be associated with the proposed change. Forexample, the report can associate a dollar cost with a direct change anda dollar cost with an indirect change. In some implementations, thedollar cost can be a parameter provided to the process. In otherimplementations, a default value can be associated with each change. Forexample, a direct change can be estimated to cost $100 and an indirectchange can be determined to cost $25.

The impact analysis approach described above can be implemented using acomputing system executing suitable software. For example, the softwarecan include procedures in one or more computer programs that execute onone or more programmed or programmable computing system (which can be ofvarious architectures such as distributed, client/server, or grid) eachincluding at least one processor, at least one data storage system(including volatile and/or non-volatile memory and/or storage elements),at least one user interface (for receiving input using at least oneinput device or port, and for providing output using at least one outputdevice or port). The software can include one or more modules of alarger program, for example, that provides services related to thedesign, configuration, and execution of dataflow graphs. The modules ofthe program (e.g., elements of a dataflow graph) can be implemented asdata structures or other organized data conforming to a data modelstored in a data repository.

The software can be provided on a tangible, non-transitory medium, suchas a CD-ROM or other computer-readable medium (e.g., readable by ageneral or special purpose computing system or device), or delivered(e.g., encoded in a propagated signal) over a communication medium of anetwork to a tangible, non-transitory medium of a computing system whereit is executed. Some or all of the processing can be performed on aspecial purpose computer, or using special-purpose hardware, such ascoprocessors or field-programmable gate arrays (FPGAs) or dedicated,application-specific integrated circuits (ASICs). The processing can beimplemented in a distributed manner in which different parts of thecomputation specified by the software are performed by differentcomputing elements. Each such computer program is preferably stored onor downloaded to a computer-readable storage medium (e.g., solid statememory or media, or magnetic or optical media) of a storage deviceaccessible by a general or special purpose programmable computer, forconfiguring and operating the computer when the storage device medium isread by the computer to perform the processing described herein. Theinventive system can also be considered to be implemented as a tangible,non-transitory medium, configured with a computer program, where themedium so configured causes a computer to operate in a specific andpredefined manner to perform one or more of the processing stepsdescribed herein.

A number of embodiments of the invention have been described.Nevertheless, it is to be understood that the foregoing description isintended to illustrate and not to limit the scope of the invention,which is defined by the scope of the following claims. Accordingly,other embodiments are also within the scope of the following claims. Forexample, various modifications can be made without departing from thescope of the invention. Additionally, some of the steps described abovecan be order independent, and thus can be performed in an orderdifferent from that described.

What is claimed is:
 1. A computer-implemented method, the methodincluding: receiving information about at least two logical datasets,the logical dataset information identifying, for each logical dataset,an identifier for at least one field in that logical dataset, and formatinformation about that field; receiving information about atransformation, the information identifying a first logical dataset fromwhich the transformation is to receive data and a second logical datadataset to which the transformed data is to be provided; receiving oneor more proposed changes to a field in the first logical dataset, afield in the second logical dataset, or a change to the transformationdataset; analyzing the one or more proposed changes based on informationabout the transformation and information about the first logical datasetand the second logical dataset; calculating one or more metrics of theproposed change based on the analysis; and storing information about theone or more metrics.
 2. The method of claim 1, wherein the calculatedmetric provides a measure of direct impact.
 3. The method of claim 1,wherein the calculated metric provides a measure an indirect impact. 4.The method of claim 1, wherein the transformation includes one or morerules to be applied to data from the first logical dataset, and whereinanalyzing the one or more proposed changes is further based on the oneor more rules.
 5. The method of claim 1, further comprising associatinga cost to the proposed change based on the metric.
 6. A systemcomprising: one or more computers and one or more storage devicesstoring instructions that are operable, when executed by the one or morecomputers, to cause the one or more computers to perform operationscomprising: receiving information about at least two logical datasets,the logical dataset information identifying, for each logical dataset,an identifier for at least one field in that logical dataset, and formatinformation about that field; receiving information about atransformation, the information identifying a first logical datasetdescribing characteristics of a first physical dataset from which thetransformation is to receive data and a second logical data describingcharacteristics of a second physical dataset to which the transformeddata is to be provided; receiving one or more proposed changes to afield in the first logical dataset, a field in the second logicaldataset, or a change to the transformation dataset; analyzing the one ormore proposed changes based on information about the transformation andinformation about the first logical dataset and the second logicaldataset; calculating one or more metrics of the proposed change based onthe analysis; and storing information about the one or more metrics. 7.The system of claim 7, wherein the calculated metric provides a measureof direct impact.
 8. The system of claim 7, wherein the calculatedmetric provides a measure an indirect impact.
 9. The system of claim 7,wherein the proposed change is one of the group consisting of a changein format of a field in a dataset or a change in a transformation. 10.The system of claim 7, wherein the transformation includes one or morerules to be applied to data from the first physical dataset, and whereinanalyzing the one or more proposed changes is further based on the oneor more rules.
 11. The system of claim 7, further comprising associatinga cost to the proposed change based on the metric.
 12. A systemcomprising: means for receiving information about at least two logicaldatasets, the logical dataset information identifying, for each logicaldataset, an identifier for at least one field in that logical dataset,and format information about that field; means for receiving informationabout a transformation, the information identifying a first logicaldataset describing characteristics of a first physical dataset fromwhich the transformation is to receive data and a second logical datadescribing characteristics of a second physical dataset to which thetransformed data is to be provided; means for receiving one or moreproposed changes to a field in the first logical dataset, a field in thesecond logical dataset, or a change to the transformation dataset; meansfor analyzing the one or more proposed changes based on informationabout the transformation and information about the first logical datasetand the second logical dataset; means for calculating one or moremetrics of the proposed change based on the analysis; and means forstoring information about the one or more metrics.
 13. A computerstorage medium encoded with computer program instructions that whenexecuted by one or more computers cause the one or more computers toperform operations comprising: receiving information about at least twological datasets, the logical dataset information identifying, for eachlogical dataset, an identifier for at least one field in that logicaldataset, and format information about that field; receiving informationabout a transformation, the information identifying a first logicaldataset describing characteristics of a first physical dataset fromwhich the transformation is to receive data and a second logical datadescribing characteristics of a second physical dataset to which thetransformed data is to be provided; receiving one or more proposedchanges to a field in the first logical dataset, a field in the secondlogical dataset, or a change to the transformation dataset; analyzingthe one or more proposed changes based on information about thetransformation and information about the first logical dataset and thesecond logical dataset; calculating one or more metrics of the proposedchange based on the analysis; and storing information about the one ormore metrics.
 14. The medium of claim 14, where the calculated metricprovides a measure of direct impact.
 15. The medium of claim 14, wherethe calculated metric provides a measure an indirect impact.
 16. Themedium of claim 14, wherein the proposed change is one of the groupconsisting of a change in format of a field in a dataset or a change ina transformation.
 17. The medium of claim 14, wherein the transformationincludes one or more rules to be applied to data from the first physicaldataset, and wherein analyzing the one or more proposed changes isfurther based on the one or more rules.
 18. The medium of claim 14,further comprising associating a cost to the proposed change based onthe metric.